Associations of FTO and MC4R SNPs with obesity traits in the

Indian Migration Study

Amy Taylor

Background

• Large number of genetic variants associated with obesity traits have been discovered in Europeans

• FTO and MC4R associations well established

• Few studies conducted in Indian populations

• Suggestion of smaller effect of FTO on BMI in a previous study in Pune (western India)

Background• Effects of FTO on obesity traits may be modified by

energy intake and/or expenditure

• But effect modification not consistently replicated across studies

• May reflect true population differences, study sample size or inter-study measurement differences

• Within population variation of environmental factors may not be large enough

Background• India is currently experiencing rapid urbanisation

• In 2001, almost a third of the population lived in urban areas, but it is estimated that by 2025, half the population will be urban dwellers*

• Urban living in India associated with lifestyle changes and increases in obesity and diabetes

• Rural/urban living may be a sufficiently strong exposure to show large interaction effects with genetic factors

*Yadav K, Krishnan A. Obes Rev 2008; 9(5):400-408.

Aim

• To replicate associations of key variants in FTO and MC4R

and

To investigate whether urban/rural environment alters the strength of these associations in an Indian population

Study population• Indian Migration Study (2005-2007)• Four cities across India:

Lucknow, Nagpur, Hyderabad,

Bangalore

• Sibling pair design:

- Factory workers and spouses who had migrated from rural areas and their siblings who had remained in rural areas

- Lifelong urban dwelling factory workers and their siblings

Methods• Study participants attended a clinic at the

factory:

Questionnaire- lifestyle and demographics

Anthropometry- height, weight, circumferences, skinfolds

Blood pressure

Fasting blood tests- lipid, glucose

Methods- Genetics• Pool of 59 SNPs analysed at Centre for

Cellular and Molecular Biology

• Sequenom based mass array assay

• 3 obesity related SNPs

- FTO rs9939609

- MC4R rs17782313, rs12970134 (r2=0.90)

Methods: Statistical Analysis• Key methodological considerations:

- Sibling pairs

- Population stratification - Effect Modification

• STATA, QTDT, MX, UNPHASED

• HWE calculated on half sibling pairs on whole sample and by city

• Obesity traits converted to age, sex adjusted

Z-scores

Methods Statistical Analysis• STATA analyses• Additive genetic model assumed• Multilevel model • Sibling pair as random effect, city as a fixed effect• Genetic effect is decomposed into between- and

within-family effects*

• Inference performed on the within-family effect • Robust to population stratification

* Fulker et al. Am J Hum Genet 1999; 64(1):259-267.

Methods: Statistical Analysis• Effect modification- added an interaction term to

the model

• Rural and urban living -defined according to current dwelling

• Dietary fat intake (from food frequency questionnaire)

• Physical activity measures (METs and time spent in moderate- vigorous physical activity)

Results6942 with genotype

data

6780 individuals3390 pairs

241rural rural pairs

162unrelated

individuals

1997rural urban pairs

1152urban urban pairs

Results  SNP

numberGenotype Frequencies

N (%)Minor Allele Frequency

HWE p value

FTO rs9939609 TT TA AA    

    1504 (44.7) 1511 (44.9) 350 (10.4) 0.33 0.31

MC4R rs12970134 GG GA AA    

    1,470 (43.2) 1,534 (45.0) 403 (11.8) 0.36 0.94

MC4R rs17782313 TT TC CC    

    1,412 (41.7) 1,544 (45.6) 431(12.7) 0.34 0.79

• In Bangalore sample, there was evidence of deviation from HWE for rs9939609

Results 

All Males

 

Females

Urban Rural Urban Rural

N 6780 2276 1649 2025 830

Sex (% Female) 42

% Urban 63        

Age (years) 40.7 42.9 39.5 39.5 40.2

BMI (kg/m2) 23.8 24.3 21.7 25.4 22.6

Waist (cm) 82.3 87.9 80.5 80.2 75.2

Body fat (%) 26.8 25.9 20.8 31.9 29.1

% Diabetic 6.8 9.8 3.6 7.1 4.3

Dietary Fat (g/day) 83.1 96.0 79.9 79.6 62.7

Daily average METs 38.8 38.4 41.2 37.5 38.4

Results: FTO Main effects

BMI

WHR

Waist

Weight

Hip

Bodyfat

Trait

0.08 (0.02, 0.14)

0.01 (-0.05, 0.07)

0.04 (-0.02, 0.11)

0.09 (0.03, 0.15)

0.05 (-0.01, 0.11)

0.02 (-0.04, 0.08)

ES (95% CI)

0.08 (0.02, 0.14)

0.01 (-0.05, 0.07)

0.04 (-0.02, 0.11)

0.09 (0.03, 0.15)

0.05 (-0.01, 0.11)

0.02 (-0.04, 0.08)

ES (95% CI)

0-.15 0 .15

Per minor allele change (SD)

rs9939609

Results: MC4R Main effects

BMI

WHR

Waist

Weight

Hip

Bodyfat

Trait

0.04 (-0.01, 0.10)

-0.01 (-0.07, 0.05)

0.04 (-0.02, 0.10)

0.06 (0.00, 0.12)

0.06 (0.01, 0.12)

0.05 (-0.01, 0.11)

ES (95% CI)

0.04 (-0.01, 0.10)

-0.01 (-0.07, 0.05)

0.04 (-0.02, 0.10)

0.06 (0.00, 0.12)

0.06 (0.01, 0.12)

0.05 (-0.01, 0.11)

ES (95% CI)

0-.15 0 .15

Per minor allele change (SD)

rs17782313

FTO: Interactions with dwelling-.

10

.1.2

Per

min

or a

llele

cha

nge

(SD

sco

re)

BMI WHR Waist Weight Hip Bodyfat Trait

Rural Urban

P for interaction between FTO and dwelling on weight =0.03

Results• No evidence for effect modification by

dwelling on associations with MC4R SNPs

• No evidence for effect modification by physical activity or dietary measures

Discussion• Effect estimates for associations of BMI with

FTO and MC4R generally in concordance with European and Indian estimates

• Effect of FTO strongest on weight and BMI- less evidence for association with regional measures of adiposity/total body fat

• Is this evidence that FTO more important for overall body size in Indians?

• May reflect accuracy of regional measures

Discussion• Possible effect modification by rural/urban

environment

• Siblings share early life environment – suggests that effect modification relates to environmental factors post migration

• Probably insufficient power to investigate interactions with physical activity/diet

Further work• Investigate associations of recently discovered

genetic variants of regional adiposity in Europeans

• Meta analyses of FTO and MC4R associations in Indian populations

• Replication of effect modification by environmental exposures in other Indian populations

Acknowledgements

University of Bristol Dave EvansNic TimpsonKate TillingYoav Ben-ShlomoGeorge Davey-Smith

Centre for Cellular and Molecular Biology, HyderabadGiriraj ChandakM.N. SandeepC.S.JanipalliM. ArunaM.V. Kranthi KumarD.G. VinayP. Smitha

LSHTMShah EbrahimClaudia GiambartolomeiSanjay KinraRuth SullivanLiza BowenFrank Dudbridge

South Asian Network for Chronic DiseaseVipin Gupta

Public Health Foundation of IndiaK.S. ReddyD. Prabhakaran

• Indian Migration Study Group, field staff and participants